{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyOzAPGOqnJRb34L49Ca0lTQ",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/LuisGSVasconcelos/Redes_Neurais_PPGEQ_UFCG/blob/main/Victor/3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data centering & scaling"
      ],
      "metadata": {
        "id": "PtLd5iWWb_HG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Standard scaling\n",
        "import numpy as np\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "X = np.array([[ 1000, 0.01,  300],\n",
        "              [ 1200,  0.06,  350],\n",
        "              [ 1500,  0.1, 320]])\n",
        "scaler = StandardScaler().fit(X) # computes mean & std column-wise\n",
        "X_scaled = scaler.transform(X) # transform using computed mean and std\n",
        "\n",
        "# check mean = 0 and variance = 1 for every variable/column after scaling\n",
        "print(X_scaled.mean(axis=0)) # return 1D array of size(3,1)\n",
        "print(X_scaled.std(axis=0)) # return 1D array of size(3,1)\n",
        "\n",
        "# access mean and variance via object properties\n",
        "print(scaler.mean_) # return 1D array of size(3,1)\n",
        "print(scaler.var_) # return 1D array of size(3,1)"
      ],
      "metadata": {
        "id": "WDJm0h3gLXcj",
        "outputId": "1f001bce-8ced-48f7-8d0d-3e21d3edfee8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[3.70074342e-16 7.40148683e-17 9.43689571e-16]\n",
            "[1. 1. 1.]\n",
            "[1.23333333e+03 5.66666667e-02 3.23333333e+02]\n",
            "[4.22222222e+04 1.35555556e-03 4.22222222e+02]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Normalization\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "scaler = MinMaxScaler() # create object\n",
        "X_scaled = scaler.fit_transform(X) # fit & transform\n",
        "\n",
        "# check min = 0 and max = 1 for every variable/column after scaling\n",
        "print(X_scaled.min(axis=0))\n",
        "print(X_scaled.max(axis=0))\n",
        "\n",
        "# access min and max via object properties\n",
        "print(scaler.data_min_)\n",
        "print(scaler.data_max_)"
      ],
      "metadata": {
        "id": "IcjfOCZtL7q5",
        "outputId": "d73e5c47-125b-4674-b096-8327221032fa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0. 0. 0.]\n",
            "[1. 1. 1.]\n",
            "[1.e+03 1.e-02 3.e+02]\n",
            "[1.5e+03 1.0e-01 3.5e+02]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Robust Centering & Scaling"
      ],
      "metadata": {
        "id": "E77lC3lsL_y9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate oulier-infested data\n",
        "X = np.random.normal(40, 1, (1500,1))\n",
        "X[200:300] = X[200:300] +8; X[1000:1150] = X[1000:1150] + 8\n",
        "\n",
        "# plot\n",
        "import matplotlib.pyplot as plt\n",
        "plt.plot(X, '.-')\n",
        "plt.xlabel('sample #'), plt.ylabel('variable measurement')\n",
        "plt.title('Raw measurements')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "gtJWoJGyL_kY",
        "outputId": "78f070e1-b544-4151-cc4b-c68bd8401f28",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        }
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Transform via standard scaling\n",
        "scaler = StandardScaler().fit(X)\n",
        "X_scaled = scaler.transform(X)\n",
        "\n",
        "# mean and std\n",
        "print('Estimated mean = ', scaler.mean_[0])\n",
        "print('Estimated standard deviation = ', np.sqrt(scaler.var_[0]))\n",
        "\n",
        "# plot\n",
        "plt.figure()\n",
        "plt.plot(X_scaled, '.-')\n",
        "plt.xlabel('sample #'), plt.ylabel('scaled variable measurement')\n",
        "plt.xlim((0,1500))\n",
        "plt.title('Standard scaling')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "rMuCuyVeMDKc",
        "outputId": "516afc19-098d-4263-ef16-a2d3ba36fcfd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        }
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Estimated mean =  41.35478841713105\n",
            "Estimated standard deviation =  3.1671050868599746\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Transform via robust MAD scaling\n",
        "# compute median and MAD\n",
        "from scipy import stats\n",
        "median = np.median(X)\n",
        "# Changed function name from median_absolute_deviation to median_abs_deviation\n",
        "MAD = stats.median_abs_deviation(X)\n",
        "\n",
        "# scale\n",
        "X_scaled = (X - median)/MAD[0]\n",
        "\n",
        "# median and MAD\n",
        "print('Estimated robust location = ', median)\n",
        "print('Estimated robust spread = ', MAD)\n",
        "\n",
        "# plot\n",
        "plt.figure()\n",
        "plt.plot(X_scaled, '.-')\n",
        "plt.xlabel('sample #'), plt.ylabel('scaled variable measurement')\n",
        "plt.xlim((0,1500))\n",
        "plt.title('Robust MAD scaling')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "EobFWw9oNf0W",
        "outputId": "d50aeb45-0a57-45fa-a65a-befa86c4841e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        }
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Estimated robust location =  40.237958558174036\n",
            "Estimated robust spread =  [0.86413242]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Feature engineering"
      ],
      "metadata": {
        "id": "hCwnxhhbVtb2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# read data\n",
        "import numpy as np\n",
        "data = np.loadtxt('quadratic_raw_data.csv', delimiter=',')\n",
        "x = data[:,0,None]; y = data[:,1,None]\n",
        "\n",
        "# plot\n",
        "import matplotlib.pyplot as plt\n",
        "plt.figure()\n",
        "plt.plot(x,y, 'o')\n",
        "plt.xlabel('x'), plt.ylabel('y')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "mv0-wQrqVumb",
        "outputId": "ae1d2461-9f5f-40a0-cc25-05071e7710ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        }
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# generate quadratic features\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "\n",
        "poly = PolynomialFeatures(degree=2, include_bias=False)\n",
        "X_poly = poly.fit_transform(x) # X_poly: 1st column is x, 2nd column is x^2"
      ],
      "metadata": {
        "id": "9QaW4FoSWfp5"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# scale model inputs\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X_poly)"
      ],
      "metadata": {
        "id": "AwyJevcdWl6T"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# linear fit & predict\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "model = LinearRegression()\n",
        "model.fit(X_poly, y)\n",
        "y_predicted = model.predict(X_poly)"
      ],
      "metadata": {
        "id": "EXlbGDbEWmWZ"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# check predictions\n",
        "# plot\n",
        "plt.figure()\n",
        "plt.plot(x,y, 'o', label='raw data')\n",
        "plt.plot(x,y_predicted, label='quadratic fit')\n",
        "plt.legend()\n",
        "plt.xlabel('x'), plt.ylabel('y')\n",
        "plt.show()\n",
        "\n",
        "# accuracy\n",
        "from sklearn.metrics import r2_score\n",
        "print('Fit accuracy = ', r2_score(y, y_predicted))"
      ],
      "metadata": {
        "id": "Jxv2_pA_Wp8-",
        "outputId": "c8dc43fc-c89e-4342-929d-07bc038bca8b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        }
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fit accuracy =  0.9161437726284858\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### One-hot encoding"
      ],
      "metadata": {
        "id": "B79glFVLVvRW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "x = np.array([['type A'],\n",
        "              ['type C'],\n",
        "              ['type B'],\n",
        "              ['type C']])\n",
        "# In newer versions of scikit-learn, OneHotEncoder always returns a sparse matrix.\n",
        "# To get a dense array, use the toarray() method.\n",
        "ohe = OneHotEncoder(handle_unknown='ignore') # sparse=False is deprecated\n",
        "X_encoded = ohe.fit_transform(x).toarray() # x in numerical form\n",
        "\n",
        "print(X_encoded)\n",
        "print(ohe.categories_)"
      ],
      "metadata": {
        "id": "cwRApqgGW_k7",
        "outputId": "2b7a984e-fb18-4721-ad16-d5c559d10584",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1. 0. 0.]\n",
            " [0. 0. 1.]\n",
            " [0. 1. 0.]\n",
            " [0. 0. 1.]]\n",
            "[array(['type A', 'type B', 'type C'], dtype='<U6')]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Workflow automation via pipelines"
      ],
      "metadata": {
        "id": "Bfkf2kgSbatL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# read data\n",
        "import numpy as np\n",
        "data = np.loadtxt('quadratic_raw_data.csv', delimiter=',')\n",
        "x = data[:,0,None]; y = data[:,1,None]"
      ],
      "metadata": {
        "id": "atLgzAqeckbo"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# create pipeline for quadratic fit via linear model\n",
        "# import relevant classes\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "# add transformers and estimators sequentially as list of tuples\n",
        "# the names ‘poly’, ‘scaler’, ‘model’ can be used to access the individual elements of pipeline later\n",
        "pipe = Pipeline([('poly', PolynomialFeatures(degree=2, include_bias=False)),\n",
        "                 ('scaler', StandardScaler()),\n",
        "                 ('model', LinearRegression())])"
      ],
      "metadata": {
        "id": "cPOFDIptcm69"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# fit pipeline and predict\n",
        "pipe.fit(x, y)\n",
        "y_predicted = pipe.predict(x)"
      ],
      "metadata": {
        "id": "qh5kLWQFco2T"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# check predictions\n",
        "# plot\n",
        "from matplotlib import pyplot as plt\n",
        "plt.figure()\n",
        "plt.plot(x,y, 'o', label='raw data')\n",
        "plt.plot(x,y_predicted, label='quadratic fit')\n",
        "plt.legend()\n",
        "plt.xlabel('x'), plt.ylabel('y')\n",
        "plt.show()\n",
        "\n",
        "# performance metric\n",
        "from sklearn.metrics import r2_score\n",
        "print('Fitting metric (R2) = ', r2_score(y, y_predicted))\n",
        "\n",
        "# performance metric vis score method\n",
        "print('Fitting metric (R2) = ', pipe.score(x,y)) # no need to import metrics module"
      ],
      "metadata": {
        "id": "t7K9iy3ocqUW",
        "outputId": "022300a4-4aac-4388-8004-2dd8907f8937",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        }
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting metric (R2) =  0.9161437726284858\n",
            "Fitting metric (R2) =  0.9161437726284858\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Holdout method / Cross-validation"
      ],
      "metadata": {
        "id": "y6jNU1nbGSRg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# read data\n",
        "import numpy as np\n",
        "data = np.loadtxt('quadratic_raw_data.csv', delimiter=',')\n",
        "x = data[:,0,None]; y = data[:,1,None]"
      ],
      "metadata": {
        "id": "H360FmT8GQ2z"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# create pipeline for quadratic fit via linear model\n",
        "# import relevant classes\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "# add transformers and estimators sequentially as list of tuples\n",
        "# the names ‘poly’, ‘scaler’, ‘model’ can be used to access the individual elements of pipeline later\n",
        "pipe = Pipeline([('poly', PolynomialFeatures(degree=2, include_bias=False)),\n",
        "                 ('scaler', StandardScaler()),\n",
        "                 ('model', LinearRegression())])"
      ],
      "metadata": {
        "id": "rR0inzEWGfIZ"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# separate training data\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=1)\n",
        "\n",
        "print('Number of samples in training set: ', x_train.shape[0])\n",
        "print('Number of samples in test set: ', x_test.shape[0])"
      ],
      "metadata": {
        "id": "1paToBzyGvTE",
        "outputId": "11cac4a5-0f18-4e3e-b87c-993b9de329ec",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of samples in training set:  20\n",
            "Number of samples in test set:  5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# fit pipeline and predict\n",
        "pipe.fit(x_train, y_train)\n",
        "y_predicted_train = pipe.predict(x_train)\n",
        "y_predicted_test = pipe.predict(x_test)"
      ],
      "metadata": {
        "id": "95-SsbdWbeFj"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# performance metrics\n",
        "from sklearn.metrics import mean_squared_error as mse\n",
        "\n",
        "print('Training metric (mse) = ', mse(y_train, y_predicted_train))\n",
        "print('Test metric (mse) = ', mse(y_test, y_predicted_test))"
      ],
      "metadata": {
        "id": "rp6_1Xw8G31x",
        "outputId": "2f067a23-b5b2-45dc-c609-a599c85057c6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training metric (mse) =  3.789080699843305\n",
            "Test metric (mse) =  3.5558636543779985\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot predictions\n",
        "y_predicted = pipe.predict(x)\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "plt.figure()\n",
        "plt.plot(x_train,y_train, 'bo', label='raw training data')\n",
        "plt.plot(x_test,y_test, 'ro', label='raw test data')\n",
        "plt.plot(x,y_predicted, color='orange', label='quadratic fit')\n",
        "plt.legend()\n",
        "plt.xlabel('x'), plt.ylabel('y')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "VzGogK-8G9VV",
        "outputId": "dd940f1d-9701-4930-9c92-d3b2230ffacb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        }
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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mIzU11SXncycKMs7WzlxQkvRpcDzVJaesIn93RMTZkpIgNtZcn2XgQPM+Ntbc7iK5ubkuO5d4JgUZZwvrCA3+DBjgggUl3eDvjohUBklJMGAA7N1bdPu+feZ2J/1RiY+PZ/jw4YwcOZLatWvTt29fAN577z1at25NjRo1iImJ4bHHHuPUqVMAGIZBeHg4s2fPLjxOu3btiLpoSvaffvoJX19fTp8+XeycL7/8Mp999hnz58/HZrNhs9lITk4GYM+ePdx1112EhoYSFhZG//79SU9PL3xvcnIyXbp0oUaNGoSGhtKjRw927drFlClTeOWVV/j1118Lj3npKtMF7HY7o0aNIjQ0lFq1avHMM89w6TKI3333HT179izc5+abb2bnzp2Frzds2BCA9u3bY7PZChfJXL16NTfccAO1a9cmJCSE3r1788svv5TvP4aHUJBxhTavg60aZHwHB3502mks+rsjIpWN3Q4jRpQ8jXnBtpEjndbc+9lnn+Hj48OyZcv46KOPAPDy8mLixIls2rSJzz77jB9//JFnnnkGMKfq79WrV2H4OH78OFu2bOHMmTNs3boVgCVLltC5c2cCAgKKnW/06NHcdddd9OvXj4yMDDIyMujevTt5eXn07duXoKAgUlJSWLZsGYGBgfTr14/c3FzOnTvHbbfdRu/evVm/fj0rVqzgoYcewmazcffdd/PUU09x7bXXFh7z7rvvLvHnHTduHFOmTOHTTz/lp59+4tixY8ydO7fIPtnZ2YwaNYo1a9awaNEivLy8uP3228nPzwfg559/BmDhwoVkZGSQdP4P/smTJxk0aBA//fQTK1eupGnTpvzxj3/k5MmTV/lfyY0YlVxmZqYBGJmZmdYWsnq4YUzDMP7X2TDy8x1++HPnDCM62jDMvzIl38LDDSMnx+GnFhE3dObMGWPz5s3GmTNnKv7mxYsv/8ek4LZ4saPLNnr37m20b9++zP1mzZpl1KpVq/D5xIkTjWuvvdYwDMOYN2+e0bVrV6N///7Ghx9+aBiGYSQkJBjPP/98qccbNGiQ0b9//yLbpk6dajRv3tzIv+hvdk5OjuHv7298//33xtGjRw3ASE5OLvGYL730ktG2bdsyf5aoqCjjnXfeKXyel5dnREdHF6vnYocPHzYAY8OGDYZhGEZaWpoBGOvWrbvsuex2uxEUFGR89dVXZdblCpf7PS3v97daZFyl1YtQLdBpC0qmpBRvibnU4cPmrMRqmRGRy8rIcOx+FdSxY8di2xYuXMj1119PvXr1CAoK4v777+fo0aOFl4p69+7N5s2bOXz4MEuWLCE+Pp74+HiSk5PJy8tj+fLlhZdbyuvXX39lx44dBAUFERgYSGBgIGFhYZw9e5adO3cSFhbG4MGD6du3L7fccgsTJkwgo4KfSWZmJhkZGXTt2rVwW7Vq1ejUqVOR/bZv384999xDo0aNCA4OJjY2FoDdu3df9vgHDx5k6NChNG3alJCQEIKDgzl16lSZ7/MkCjKu4hcBLUebj3993hzJ5EDl/X/nyBFdZhKRMlzUt8Qh+1VQjRo1ijxPT0/n5ptvpk2bNsyZM4e1a9cyadIk4EJn4NatWxMWFsaSJUuKBJklS5awevVq8vLy6N69e4XqOHXqFB07diQ1NbXI7bfffmPgwIEATJ48mRUrVtC9e3c+//xzmjVrxsqVKx3wKRR1yy23cOzYMf7973+zatUqVq1aBZTdGXrQoEGkpqYyYcIEli9fTmpqKrVq1apUnagVZFypxSgz0DhhQcmK/j1x4uVtEfF0cXHmInKXrpBbwGaDmBhzPxdYu3Yt+fn5jBs3juuuu45mzZqxf//+S0qyERcXx/z589m0aRM9e/akTZs25OTk8PHHH9OpU6diAeliPj4+2C/5o9ihQwe2b99OREQETZo0KXILCQkp3K99+/aMGTOG5cuX06pVK6ZPn17qMS8VEhJCVFRUYTABOHfuHGvXri18fvToUbZt28YLL7zA9ddfT8uWLTl+/Hix+oFi51u2bBlPPPEEf/zjH7n22mvx9fXlyJEjl63J0yjIuFKRBSVfhrxTDjt0WX93LmYYsGePeTlKRKQYb2+YMMF8fOkflYLn48eb+7lAkyZNyMvL44MPPuD3339n6tSphZ2ALxYfH8+MGTNo164dgYGBeHl50atXL6ZNm0bv3r0ve47Y2FjWr1/Ptm3bOHLkCHl5edx7773Url2b/v37k5KSQlpaGsnJyTzxxBPs3buXtLQ0xowZw4oVK9i1axc//PAD27dvp2XLloXHTEtLIzU1lSNHjpCTk1PiuUeMGMFbb73FvHnz2Lp1K4899hgnTpwofL1mzZrUqlWLf/3rX+zYsYMff/yRUaNGFTlGREQE/v7+fPfddxw8eJDMzEwAmjZtytSpU9myZQurVq3i3nvvxd/fvyIfv9tTkHG1xkMhsDGcPQjbxjvssBf/3SkvJ13eFpHKIDERZs82O9ZdLDra3F5kpVznatu2Le+99x5vv/02rVq1Ytq0aYwdO7bYfr1798ZutxfpCxMfH19sW0mGDh1K8+bN6dSpE+Hh4SxbtoyAgACWLl1K/fr1SUxMpGXLlgwZMoSzZ88SHBxMQEAAW7du5Y477qBZs2Y89NBDDBs2jIcffhiAO+64g379+tGnTx/Cw8OZMWNGied+6qmnuP/++xk0aBDdunUjKCiI22+/vfB1Ly8vZs6cydq1a2nVqhVPPvkk7777bpFjVKtWjYkTJ/Lxxx9Tt25d+vfvD8Ann3zC8ePH6dChA/fffz9PPPEEERER5fnYPYbNMEoaX1d5ZGVlERISQmZmJsHBwVaXY0qfaS4oWS0Ibt0JfuEOO3RSEjzyiNmxtyyLF5ex+KWIeKyzZ8+SlpZGw4YN8fPzu/ID2e1m821GhnkNOy7OZS0xUvld7ve0vN/fapGxghMXlExMNEcv1b7MYtsuvrwtIp7M29v8F88995j3CjHiZhRkrFBkQcl/OnxBSR8f+PhjM7C4weVtERERp1GQsUpkAkTecH5ByRcdfng3urwtIiLiNAoyVnLygpKJiZCebvaFmT7dvE9LU4gREZHKo5rVBVRpYR2gwT2wa4a5oGSf/zn8FAWXt0VERCojtchYre3r4FXd6QtKioiIVEYKMlYLbARNHjEfpz5b8mqzIiIiUiIFGXfQ6oXzC0qugT2zra5GRETEYyjIuIOLF5RMdfyCkiIiIpWVgoy7KFhQ8tQO2Pkfq6sREalyXn75Zdq1a+e048fHxzNy5Mgrfv/p06e54447CA4OxmazceLECWJjYxk/frzDavRECjLuosiCkq84dEFJERFxneTk5MKgcbGkpCRee+21Kz7uZ599RkpKCsuXLycjI4OQkBBWr17NQw89VLiPzWZj3rx5V3wOT6Qg404uXlBy6/tWVyMiIhfJzc29qveHhYURFBR0xe/fuXMnLVu2pFWrVkRGRmKz2QgPDycgIOCq6vJ0CjLuxNsH2p5fe2nLO3C2HCs/iohUItnZ2TzwwAMEBgYSFRXFuHHjil2SKanVITQ0lClTphQ+f/bZZ2nWrBkBAQE0atSIF198kby8ov0P33rrLerUqUNQUFDhqtYXGzx4MLfddhtvvPEGdevWpXnz5gBMnTqVTp06ERQURGRkJAMHDuTQoUMApKen06dPHwBq1qyJzWZj8ODBQPFLSzk5OTz77LPExMTg6+tLkyZN+OSTT0r8XOLj4xk3bhxLly7FZrMVruZ98aWl2NhYAG6//XZsNlvh88pOE+K5m/p3wpZ34dha2Pg6dJpgdUUiUhkYBthPW3Nu74DiC7+V4umnn2bJkiXMnz+fiIgInn/+eX755ZcK910JCgpiypQp1K1blw0bNjB06FCCgoJ45plnAPjiiy94+eWXmTRpEj179mTq1KlMnDiRRo0aFTnOokWLCA4OZsGCBYXb8vLyeO2112jevDmHDh1i1KhRDB48mG+//ZaYmBjmzJnDHXfcwbZt2wgODsbf37/EGh944AFWrFjBxIkTadu2LWlpaRw5cqTEfZOSknjuuefYuHEjSUlJ+Pj4FNtn9erVREREMHnyZPr164d3FVlQT0HG3di8oN3b8GMC7PgQWoyEwIZWVyUins5+Gr4ItObcd52CajXK3O3UqVN88skn/Pe//+X6668HzH4h0dHRFT7lCy+8UPg4NjaW0aNHM3PmzMIgM378eIYMGcKQIUMAeP3111m4cGGxVpkaNWrwn//8p0hw+Mtf/lL4uFGjRkycOJHOnTtz6tQpAgMDCQsLAyAiIoLQ0NAS6/vtt9/44osvWLBgAQkJCYXHKk1YWBgBAQH4+PgQGRlZ4j7h4eGA2TpV2j6VkS4tuaPI6yHyRqctKCki4o527txJbm4uXbt2LdwWFhZWeEmnIj7//HN69OhBZGQkgYGBvPDCC+zevbvw9S1bthQ5D0C3bt2KHad169bFWj/Wrl3LLbfcQv369QkKCqJ3794ARY5fltTUVLy9vQvfK1dOLTLuqt1b8N0P5oKSLZ6CsPZWVyQinsw7wGwZsercDmSz2TAumQX94v4vK1as4N577+WVV16hb9++hISEMHPmTMaNG1fhc9WoUbQlKTs7m759+9K3b1+mTZtGeHg4u3fvpm/fvhXqDFza5SapOLXIuKuw9tBgoPn41zHW1iIins9mMy/vWHErZ/+Yxo0bU716dVatWlW47fjx4/z2229F9gsPDycjI6Pw+fbt2zl9+kL/n+XLl9OgQQP++te/0qlTJ5o2bcquXbuKHKNly5ZFzgOwcuXKMmvcunUrR48e5a233iIuLo4WLVoUdvQtUNCCY7fbSz1O69atyc/PZ8mSJWWesyKqV69+2fNWRgoy7qzta+cXlPweDiyyuhoREacKDAxkyJAhPP300/z4449s3LiRwYMH4+VV9KvqD3/4A//4xz9Yt24da9as4ZFHHqF69eqFrzdt2pTdu3czc+ZMdu7cycSJE5k7d26RY4wYMYJPP/2UyZMn89tvv/HSSy+xadOmMmusX78+Pj4+fPDBB/z+++98+eWXxeaGadCgATabja+//prDhw9z6lTxlrDY2FgGDRrEX/7yF+bNm0daWhrJycl88cUXFfnISjzuokWLOHDgAMePH7+qY3kKBRl3VmxByXxr6xERcbJ3332XuLg4brnlFhISEujZsycdO3Ysss+4ceOIiYkhLi6OgQMHMnr06CJzqdx66608+eSTDB8+nHbt2rF8+XJefLFof8O7776bF198kWeeeYaOHTuya9cuHn300TLrCw8PZ8qUKcyaNYtrrrmGt956i7///e9F9qlXrx6vvPIKzz33HHXq1GH48OElHuvDDz9kwIABPPbYY7Ro0YKhQ4eSnZ1d3o+qROPGjWPBggXExMTQvn3V6JJgMy690FjJZGVlERISQmZmJsHBwVaXU3FnD8GXjeHcKejxOTS4y7Xnt9shJQUyMiAqCuLioIoM6RPxZGfPniUtLY2GDRvi5+dndTlXJT4+nnbt2lX5qfgro8v9npb3+1stMu7OLwJaPm0+/tXFC0omJUFsLPTpAwMHmvexseZ2ERERN6Ag4wkKF5TcCTv+7ZpzJiXBgAGwd2/R7fv2mdsVZkRExA0oyHiC6oHQ6iXz8UYXLChpt8OIEeZMoJcq2DZypLmfiIiTJScn67KSlEpBxlM0GQqBTcw+M85eUDIlpXhLzMUMA/bsMfcTERGxkIKMp/Cq7roFJS+an8Eh+4mIZSr5eA7xcI74/VSQ8ST1B0BYJ3ME08bXnXeeqCjH7iciLlcwr8rFE8WJuJuC38+L5wGqKC1R4ElsXubSBYULSo4w55pxtLg4iI42O/aWlJZtNvP1uDjHn1tEHMLb25vQ0NDCWWcDAgKwlXOGXRFnMwyD06dPc+jQIUJDQ69qpW4FGU9TsKDkgR/g1xehxzTHn8PbGyZMMEcn2WxFw0zBH8Lx4zWfjIibK1gB+dIp9EXchSNW6taEeJ7o2Dr4roP5uN8vzltQMinJHL10ccffmBgzxCQmOuecIuJwdru9yKKKIu6gevXql22JKe/3t4KMp1p2L+yabrbO/OF7551HM/uKiIgFyvv9rUtLnqrta7BnlnmJ6cBCiExwznm8vSE+3jnHFhERuUoateSpAhtBk/MLnKU+pwUlRUSkSlKQ8WStXoBqQXBsLeyebXU1IiIiLqcg48n8wq1bUFJERMQNKMh4uhZPgl8d1y4oKSIi4iYsDTJLly7llltuoW7duthsNubNm1fk9cGDB2Oz2Yrc+vXrZ02x7qp6ILR24YKSIiIibsTSIJOdnU3btm2ZNGlSqfv069ePjIyMwtuMGTNcWKGHaPz/LlpQ8j2rqxEREXEZS4df33TTTdx0002X3cfX1/eqZ/2r9AoWlFx2N2x5F5o+An4RVlclIiLidG7fRyY5OZmIiAiaN2/Oo48+ytGjRy+7f05ODllZWUVuVYKrFpQUERFxI24dZPr168f//d//sWjRIt5++22WLFnCTTfdhN1uL/U9Y8eOJSQkpPAWExPjwootZPOCdm+bj3d8BCd3WluPiIiIC7jNEgU2m425c+dy2223lbrP77//TuPGjVm4cCHXX399ifvk5OSQk5NT+DwrK4uYmJjKt0RBaRb3g4zvocE90GO61dWIiIhckfIuUeDWLTKXatSoEbVr12bHjh2l7uPr60twcHCRW5XS7i3zftcMOPaLtbWIiIg4mUcFmb1793L06FGioqKsLsV91WwHsfeaj9eNBvdocBMREXEKS4PMqVOnSE1NJTU1FYC0tDRSU1PZvXs3p06d4umnn2blypWkp6ezaNEi+vfvT5MmTejbt6+VZbu/Nq+Dly8cXAx751ldjYiIiNNYGmTWrFlD+/btad++PQCjRo2iffv2/O1vf8Pb25v169dz66230qxZM4YMGULHjh1JSUnB19fXyrLdX2DshaULfnkK7GctLUdERMRZ3Kazr7OUt7NQpXMuG75qDmf2mXPMXPu81RWJiIiUW6Xs7CsVUK0GtH/HfLzpTTi9z9p6REREnEBBpjJrcA/U7m62zqQ+Z3U1IiIiDqcgU5nZbNBpImCD9P/CoRSrKxIREXEoBZnKLqwjNBlqPl4zDPLPWVuPiIiIAynIVAVt3wSfMDixAX4rfaVxERERT6MgUxX41row4++Gv8GZDGvrERERcRAFmaqi8RAI6wx5WbDuGaurERERcQgFmarC5gWdJ3Gh4+9SqysSERG5agoyVUmtztDkIfPx6mGQn2dtPSIiIldJQaaqafuG2Wcmc6M6/oqIiMdTkKlqfGtB2/Mdf9er46+IiHg2BZmqqPFfoFYXOHcS1j1tdTUiIiJXTEGmKrJ5Qed/Ynb8nQYHl1hdkYiIyBVRkKmqwjpC00fMx2vU8VdERDyTgkxV1uZ18K0NmZtg2wdWVyMiIlJhCjJVmW8YtHvbfLzhJTi939p6REREKkhBpqprNBhqXQfnTsG60VZXIyIiUiEKMlVdwYy/Ni/YNQMOLra6IhERkXJTkBEI6wBNHjUfa8ZfERHxIAoyYmr7GviGQ9YW2DbB6mpERETKRUFGTD41of075uMNL8PpvZaWIyIiUh4KMnJBwwegdjc4lw2/qOOviIi4PwUZucDmBZ3Od/zd/TkcWGR1RSIiIpelICNFhbWHpo+Zj9cMB3uutfWIiIhchoKMFNemoOPvVtg23upqRERESqUgI8X5hEL7d83HG1+F7D2WliMiIlIaBRkpWcP7IbyH2fF33VNWVyMiIlIiBRkpWZGOv7MgY4HVFYmIiBSjICOlq9kWmg43H68ZDvYca+sRERG5hIKMXF6bV8CvDpz8Dbb8HbsdkpNhxgzz3m63ukAREanKFGTk8nxCof04AOzrXye+4w769IGBA6FPH4iNhaQkSysUEZEqTEFGyhY7kINeCXhzlhdvegwwCl/atw8GDFCYERERayjISJns+TYGvPUhZ3N9ubH1Au7pPqPwNcMwb488ArmaO09ERFxMQUbKlJICP/3ahNfmvQjA+/c9SWjA8SL7HD4M0dFqmREREddSkJEyZWSY9+9+/TSb97WkTsgh3r7n2WL7HT6sy0wiIuJaCjJSpqgo8z7P7sPDn3wMwEN/+Dc9m6eUuP/IkRrNJCIirqEgI2WKizMvG9ls8NO2OD5e9BAA/xryED7Vis4tYxiwZ495OUpERMTZFGSkTN7eMGHChefPznybjOORtKy3ledufavE9xRcjhIREXEmBRkpl8REmD0bateGzNOhjJhqJpvnb32TFnW3FNu/4HKUiIiIMynISLklJprzxoSHw6xVd/L1uj/hWz2Xfw15CJstHzAvP8XEmJejREREnE1BRirExwc++ghsNhvDpvyTk2cCiWvxE48mfIjNZu4zfrx5OUpERMTZFGSkwgouM+X71ee5z80+Mm//+Vmua53O7Nnm6yIiIq5gMwzDKHs3z5WVlUVISAiZmZkEBwdbXU6lYrdDytJ8rsmIJ4IUjDo3YPvD9xQ2zYiIiFyh8n5/q0VGrpi3N8T38SLi5v+Atx+2gwvg9ylWlyUiIlWIgoxcveBm0PpV8/Evo+CMxl6LiIhrKMiIY7R4EsI6Qd4JWP2oOTOeiIiIkynIiGN4VYPrPgWv6rB3Puz+wuqKRESkClCQEccJbQ3XPG8+XvM4nD1ibT0iIlLpKciIY137PIS0gpzDsHaE1dWIiEglpyAjjuXtY15isnnBrumw9yurKxIRkUpMQUYcr1ZnaPGU+Xj1I5B7wtJyRESk8lKQEedo/QoENYUz+2Hd01ZXIyIilZSCjDhHNX/o+h/z8c7/wIFF1tYjIiKVkoKMOE9EL2j6mPl41VDIO2VtPSIiUukoyIhztXsLAupDdhr8+lerqxERkUpGQUacq3oQdP23+fi3D+DwMmvrERGRSkVBRpwv6kZoNBgwYNUQsJ+1uiIREakkFGTENTq8B36RkLUNNrxqdTUiIlJJKMiIa/jUhM7/NB9veQeO/WJtPSIiUikoyIjrxNwO9e8Cww4rHwR7rtUViYiIh1OQEdfq9AH41oIT62GjLjGJiMjVUZAR1/KLgM4fmY83j4Ujq6ytR0REPJqCjLhe/QHQ4B4w8mHlIDh3xuqKRETEQ1kaZJYuXcott9xC3bp1sdlszJs3r8jrhmHwt7/9jaioKPz9/UlISGD79u3WFCuO1ekf4B9ljmLSRHkiInKFLA0y2dnZtG3blkmTJpX4+jvvvMPEiRP56KOPWLVqFTVq1KBv376cPat5SDyebxh0Ob8W07bxcHCJpeWIiIhnshmGYVhdBIDNZmPu3LncdtttgNkaU7duXZ566ilGjx4NQGZmJnXq1GHKlCn8+c9/Ltdxs7KyCAkJITMzk+DgYGeVL1dq1VBzUckasfDH9eZMwCIiUuWV9/vbbfvIpKWlceDAARISEgq3hYSE0LVrV1asWFHq+3JycsjKyipyEzfWYRzUaADZ6bButNXViIiIh3HbIHPgwAEA6tSpU2R7nTp1Cl8rydixYwkJCSm8xcTEOLVOuUrVg+G6yebjHf+C/d9ZW4+IiHgUtw0yV2rMmDFkZmYW3vbs2WN1SVKWOn2g+Qjz8aohkHvc2npERMRjuG2QiYyMBODgwYNFth88eLDwtZL4+voSHBxc5CYeoO2bENQMzuyHnx8B9+i6JSIibs5tg0zDhg2JjIxk0aJFhduysrJYtWoV3bp1s7AycYpqAdBtKti8YfcXkDbV6opERMQDWBpkTp06RWpqKqmpqYDZwTc1NZXdu3djs9kYOXIkr7/+Ol9++SUbNmzggQceoG7duoUjm6SSqd0FWr9sPl4zHE79bmk5IiLi/qpZefI1a9bQp0+fwuejRo0CYNCgQUyZMoVnnnmG7OxsHnroIU6cOEHPnj357rvv8PPzs6pkcbZrxkDG93D4J1h+HyQsBS9Lf01FRMSNuc08Ms6ieWQ80Kl0+F9byMsyW2hav2R1RSIi4mIeP4+MVGGBsdD5Q/PxxtfgcOnzBomISNWmICPuKXYgNBgIhh2W32u2zoiIiFxCQUbcV+dJ52f9TYM1T1hdjYiIuCEFGXFfPqHnh2R7QdpnsOsLqysSERE3oyAj7i0izhzJBPDzw5CtmZpFROQCBRlxf61fgrDOkHcCVjwA+XarKxIRETehICPuz6s6dJ8G1WrAoWTYOs7qikRExE0oyIhnCG4KHSeaj9e/AMfWWluPiIi4BQUZ8RyNHoSYRMjPM4dkn8u2uiIREbGYgox4DpsNuvwL/OtC1jb45SmrKxIREYspyIhn8a0F3f7PfLzjY9j7pbX1iIiIpRRkxPNEXg8tzrfGrBoCZzKsrUdERCyjICOeqe0bENoWco7AygfByLe6IhERsYCCjHgmb1/oMR28/SDje9j6ntUViYiIBRRkxHOFXAMdzgeY1Ofg4GJr6xEREZdTkBHP1uQRiL3fXCX7p7u1hIGISBVT4SAzaNAgli5d6oxaRCrOZoMuH0PNdpBzGFLuAPtZq6sSEREXqXCQyczMJCEhgaZNm/Lmm2+yb98+Z9QlUn7V/CEuCXzC4NhqWPO41RWJiIiLVDjIzJs3j3379vHoo4/y+eefExsby0033cTs2bPJy8tzRo0iZQtsCD1mADbY+R/Y8W+rKxIRERe4oj4y4eHhjBo1il9//ZVVq1bRpEkT7r//furWrcuTTz7J9u3bHV2nSNmibjSHZQOsGQ5HVllbj4iION1VdfbNyMhgwYIFLFiwAG9vb/74xz+yYcMGrrnmGt5//31H1ShSftc8B9G3Q36u2V/mzEGrKxIRESeqcJDJy8tjzpw53HzzzTRo0IBZs2YxcuRI9u/fz2effcbChQv54osvePXVV51Rr8jl2WzQbQoEt4Az+2DZ3ZB/zuqqRETESapV9A1RUVHk5+dzzz338PPPP9OuXbti+/Tp04fQ0FAHlCdyBaoHQ9xc+L4LHFoC656BjpowT0SkMrIZhmFU5A1Tp07lzjvvxM/Pz1k1OVRWVhYhISFkZmYSHBxsdTniSnvmQkqi+bj7dIi9x9p6RESk3Mr7/V3hS0v333+/x4QYqeJibodrxpiPVw2B4+utrUdERBxOM/tK5dbmNYi8AexnzNaZ3ONWVyQiIg6kICOVm5e3Ob9MjVg4tROW36eVskVEKhEFGan8fGuZM/96+8H+b2HDK1ZXJCIiDqIgI1VDWHvo8i/z8cZXYe9X1tYjIiIOoSAjVUfD+6HZcPPxivsgSzNQi4h4OgUZqVraj4PwHpCXBSm3Q94pqysSEZGroCAjVYu3D/ScBX6RkLnJHJZdsamURETEjSjISNXjHwVxs8FWDXZ/AVvHWV2RiIhcIQUZcSm7HZKTYcYM895ut6iQ8B7QcYL5OPVZOPCjRYWIiMjVUJARl0lKgthY6NMHBg4072Njze2WaPooNBxkziuz7G7I3m1RISIicqUUZMQlkpJgwADYu7fo9n37zO2WhBmbDTp/CDXbQ84RSLkD7GctKERERK6Ugow4nd0OI0aU3Ke2YNvIkRZdZqrmb06W5xMGx9bA6mHq/Csi4kEUZMTpUlKKt8RczDBgzx5zP0sExkKPmWDzgt8/hR3/sqgQERGpKAUZcbqMDMfu5xRRN0CbN8zHax+HIystLEZERMpLQUacLirKsfs5zTXPQswdkJ9n9pc5c9DigkREpCwKMuJ0cXEQHW32rS2JzQYxMeZ+lrLZ4LrJENwCzuyHZXeZoeYyLjec3G2GmouIVGLVrC5AKj9vb5gwwRydZLMV7UtbEG7Gjzf3s1z1IIibC993gUNLYd0z0PH9EndNSjI7MV/c/yc62vxZofTX+vc3+wNlZJitUHFxbvKzi4h4IJthVO4hGllZWYSEhJCZmUlwcLDV5VRpJX3xx8SYISYx0bKySrZnLqScL6r7NIgdWOTlguHkl/7fc2lQK+m1WrXg6NEL2wsCjtt9BiIiFirv97eCjLiU3e5BrRG//hU2vQne/nDjCqjZFjB/htjYy4/EqoiCVqnZsxVmREQKKMicpyAjVyzfDkv+BBnfQ42G0G8N+IaRnGzOSuxINpvZMpOW5sbBTkTEhcr7/a3OviKl8fKG7tPNEJOdBsvvhXy7U4aJWz6XjoiIh1KQEbkc3zDolQTefpDxHWx8xanDxC2dS0dExAMpyIhbcOuhyjXbQZd/m483vkZcoy8vO5z8alg+l46IiIdRkBHLud2q2CVpeB80exwA71X388n724DiYebi5xUJOm4zl46IiIdRkBFLueWq2KXpMA7Ce0JeFjf6JzJv1knq1Su6S3Q0zJlj3i59rVYt87608OM2c+mIiHgQjVoSy5Q1jNktR/KcOQDfdYAzGRAzAHu3L0j5yVbicPKShprPn+9Bc+mIiFhIw6/PU5BxX+Udxrx4McTHO7uaCji8Ahb1NpcvaPcOXPN0hd7uUXPpiIhYpLzf31qiQCzjEatilyS8G3ScAKsfg1+fg7AOEHl9ud/u7e1mwUxExIOpj4xYxmNWxS5Jk0eg0WAw8mHZ3ZC9y+qKRESqJAUZsYzHrIpdEpsNOv0TanaAnKOwNBHOnba6KhGRKkdBRixTsCo2XNlIHsvnnqnmb06W51sLjv8Cy+8zlzUQERGXUZARSyUmmoslljSM+XKLKLrN3DM1GkDcXPDygb1zYd1TLi5ARKRq06glcQsVGclTMPfMpb+5lq4ivetzWPZn83GH96HFSBcXICJSuWj49XkKMpWLW889s/kdSH0WsEHcbIjRxDAiIldKq19LpZSSUnqIAYtXkW75NDR9FDDMlbIPr7CgCBGRqkVBRjyKW889Y7NBx4lQ92awn4Wlt8LJHRYUIiJSdSjIiEdx+7lnvKpBz5kQ1hFyjsDim+DsEYuKERGp/BRkxKN4xNwz1WpA76/NEU2ndsCSm+FctoUFiYhUXgoy4lGudu4Zl/GPhPj/gU9NOLoKfrrLXJtJREQcyq2DzMsvv4zNZitya9GihdVlicWudO4ZlwtpCb2/AW9/2P8trPp/xceMi4jIVXH7RSOvvfZaFi5cWPi8WjW3L1lcIDER+vf3gFWkw7tBzy9g6W2Q9n/gFwnt37a6KhGRSsPtU0G1atWIjIws9/45OTnk5OQUPs/KynJGWeIGPGYV6Xo3Q9f/wMoHYcs74FcHWo6yuioRkUrBrS8tAWzfvp26devSqFEj7r33Xnbv3n3Z/ceOHUtISEjhLSYmxkWVilxGo8HQ7i3z8bqnIG2apeWIiFQWbj2z7//+9z9OnTpF8+bNycjI4JVXXmHfvn1s3LiRoKCgEt9TUotMTEyMZvYV6xkG/DIKto0HWzVzZFPdvlZXJSLilirlEgUnTpygQYMGvPfeewwZMqRc79ESBeJWjHxzlexdM8A7AHp/BZF/sLoqERG3UymXKAgNDaVZs2bs2KHZUsVD2bzguikQ1Q/spyH5Jtjj6iW7RUQqD48KMqdOnWLnzp1EWTZtq4gDePtAr3nmopL5ufDTnbDjP1ZXJSLikdw6yIwePZolS5aQnp7O8uXLuf322/H29uaee+6xujSRq+PtCz2+gMZDzctNPw+FzW9rnhkRkQpy6+HXe/fu5Z577uHo0aOEh4fTs2dPVq5cSXh4uNWliVw9L2/o8jH41obNYyH1OTh7GNq/W/oaDCIiUoRHdfa9EursKx5hyzhYN9p83GgwdPm3uQCliEgVVSk7+4pUWi2fMjsB27zh9ymQcgecO2N1VSIibk9BRsRdNBoEcUng5Qv7voTkfpCbaXVVIiJuTUFGxJ1E3wp/+AGqB8OhpbAoHs4ctLoqERG3pSAj4m4iekHCEvCLgOOpsKAnnEqzuioREbekICPijmq2gxuWQY1YOLUDFvSAExutrkpExO0oyIi4q6AmZpgJaQVnMmBBHBxebnVVIiJuRUFGxJ0F1IUblkLt7pB3An5MgP3/s7oqERG3oSAj4u58apodgKNuAvsZWHIrpE+3uioREbegICPiCarVgN7zocFAMM7B8nth2wdWVyUiYjkFGRFP4VUduk+FZo+bz9c+Aetf0vpMIlKlKciIeBKbF3ScAK1fNZ9vfBXWDDcXnhQRqYIUZEQ8jc0GrV+Ezv8EbLD9n+alJnuu1ZWJiLicgoyIp2r6KPSYYV5y2jUTlt4K57KtrkpExKUUZEQ8WYO7oddX4B0AGd/Doush56jVVYmIuIyCjIinq9sXrl9kDtM+ugoW9oLT+6yuSkTEJRRkRCqD2tdBQgr414PMzeaSBlm/WV2ViIjTKciIVBah18KNyyCoGWTvMhebPPaL1VWJiDiVgoxIZVKjAdyQAjU7QM5hWBgPB5OtrkpExGkUZEQqG78ISFgMEfFw7iQs7gd75lldlYiIUyjIiFRG1YOhz/8g+jbIz4Gf7oCdn1pdlYiIwynIiFRW3n7QcxY0+os58++qIbD5HaurEhFxKAUZkcrMqxp0/Q+0fMZ8nvosrHtG6zOJSKWhICNS2dls0P5taP+u+XzLu7Dq/0H+OWvrEhFxAAUZkaqi5Wjo+qm58OTvn8JPd4L9rNVViYhcFQUZkaqk8YPQcw54+cLeebD4JsjLuqpD2u2QnAwzZpj3drsjChURKR8FGZGqJuY26PMdVAuCQ8nmXDNnD13RoZKSIDYW+vSBgQPN+0YN7Cx9NVnJRkRcQkFGpCqqEw8JyeAbDsfXmbMAn0qv0CGSkmDAANi798K220nip32x9HrpomQTG2vuLCLiBAoyIlVVWAe44SdzNuCT2831mU5sKtdb7XYYMaLo4KfbSWI2A6jH3qI779tnJh6FGRFxAgUZkaosuBncsAxCroUz+2FhHBxeUebbUlKKtsR4YWcCIwCj+B+VgrQzcqQuM4mIwynIiFR1AfUgYSnUug5yj8OPCbD/+8u+JSOj6PM4Uohhb+l/UAwD9uwxE5CIiAMpyIgI+IbB9Qshqh/YT8PSWyB9Zqm7R0Vd8pyMkne81KUJSETkKinIiIipWg3oNR8a/Bny82D5QPhtUom7xsVBdLQ51x5ABlEl7lfMpQlIROQqKciIyAXePtB9GjQdBhiwZjhseKXYkgbe3jBhgvnYZoMU4thDNPnYSj6uzQYxMWYCEpHKIz8P9ljbkV9BRkSKsnlBpw+g1Uvm8w0vw9onzIUnL5KYCLNnQ716kI83IzCTTbEwU9BsM368mYBEpHI4ewQW94WUO2DnZMvKUJARkeJsNmjzMnT8ALDBb/+A5feBPbfIbomJkJ4OixfDndMT2fzKbGzR9YoeKzraTDyJia6qXkSc7fh6+L4zHFxsXpb2CbWsFJthVO5lcLOysggJCSEzM5Pg4GCryxHxPOkzYMUDYJwzOwPHzTb/cJXGbjdHJ2VkmH1i4uLUEiNSmeyeBSsGmwMDAhuZfetCWzn8NOX9/q7m8DOLSOUSew/41ISURMj4Dn68AXp/bY50Kom3N8THu7REEXEBIx/Wvwib3jSfR94APWaW/rfARXRpSUTKVrcf/GGRGWiOrICFveD0PqurEhFXyc2EJf0vhJgWT0H8t5aHGFCQEZHyCu9mTpznHwWZm8z1mbK2W12ViDhb5mb4oSvs/xq8fKHbVOjwd/Byj4s6CjIiUn6hrcwlDQKbQHY6LOwJx9ZZXZWIOEv6DPi+C2Rtg4Boc322hvdZXVURCjIiUjGBDc0/ZjXbw9lDsCgeDi6xuioRcSR7Dqwebk6MeS4b6vwB+q6BWp0u7GKH5GSYMcO8t2opNQUZEak4/zpw/WKI6A15WeZcEnu/tLoqEXGE7N1mP7jt5sze+df8lWSvH5gxr05hYElKgthY6NMHBg4072NjrVnkXsOvReTK2c/Csj/D3vlg84b246D5ExcmwRMRz7L/e1hxL+QcBZ+aLDemcveoPxVZ7b5WLTh6tPhbC/63d9S0UeX9/laLjIhcOW8/6DkbGj0Ihh1+GWkO0849bnVlIlIR9rOQOgaSbzJDTFhHvuMXet5dNMRAySEGLqxkMnKkay8zKciIyNXxqgZdP4GOE8CrOuydB//rAIeXW12ZiJTHoRT4ti1sfgswoMkj2P/wE0OfjL10mbUyGQbs2WPOiekqCjIicvVsNvOS0g3LzZk+s9PN4dk/P6LWGRF3lZcFq4eZ/WFO/mZOrRCXBF0+JGW5X7GWmIrIyHBcmWVRkBERx6nVCfr9Ao0GAwbs+Bi+ag5pU4utoC0iFtr3DXxzLWz/p/m88f+DP22GmNuBqw8iUVFXWV8FqLOviDjHoaWw+lFzMi2AiHjo/CGEtLC0LJEq7exhWDsSdk03nwc2gi7/wh5+fZEl0ux2SEio+OFtNnOd2LS0q19iTWstiYi1InpBv3Ww9T3Y+CocSob/tYGWT8O1f4VqAVZXKFJ1GAbsmglrn4CcI2DzguZPQptXSfoygBEjKHIpqV49c3TSsWPlb0wtGLU0frxr14nVpSURcR5vH7j2ObPJuu7NkJ9nrtXyTSvY963V1YlUDdl7YMkt5uR2OUcgtDXcuBI6/J2kLwMYMIBi/WH27zdHJxlG8dkUCp7XqlV0e3S044ZeV4QuLYmIaxiGOd/M2sfh9Pm/mjF3QMfx5tTnIuJYRr7ZT23ds3DuJHj5wLUvwDXPgrcPdrs5iV1pnXptNggLA3//ovvExJitLv37U+RyVFycY1tiyvv9rSAjIq6Vdwo2vgJb3zfnnqkWCG1ehWaPu80idCIeL+s3WPX/4PD5cdC1u0HX/0DINYW7JCebM/KWZeFCM6A4K7CURn1kRMQ9VQ+E9u9C7P1mZ+Ajy+GXUfD7Z2Zn4PBuVlco4rny82DLONjwMuTnQLUa0PZNaDoMvIqmj/KOTDp0CO65x/GlOor6yIiINWq2gRtSzH8l+oTBiV9hQXf4+WHIOWZ1dSKe59g6c6XqX8eYISbyRvjjRnOOJ6/iTSjlHSLtyqHUV0JBRkSsY/OCxkPg5q3n554BdvwLvm4Bv/+f5p4RKY9zZ8zlBb7vDMdTwacmXPcZ9PkOAmNLfVtcnNlBt7Sl0Ww2sz9MXJxTqnYYBRkRsZ5fOFw3GRKWmNfwcw7DykGwqA9kbrG6OhH3dWgp/O/88gKGHerfBX/aAo0eKHPxVm9vmDDBfFzayCRXD6W+EgoyIuI+CuaeafcWePvDoSXmH+nU5+HcaaurE3EfeVnw86OwsDec3A7+daHXPOj5OfjXKfdhEhPNIdP16hXdbtVQ6iuhUUsi4p5OpZuTd+37ynxeIxY6/QPq/cnKqkSst+9rs6N8wTQGjYdC+3fAJ/SKD2m3O3co9ZXQ8OvzFGREPNze+bDmcTi9x3wefbu50naNGGvrEnG1s4dh7QjYNcN8HtgYuv4b6pRjDLUHKu/3ty4tiYh7i+5vzgzc8mmwecPeufBNS3OIaX6e1dWJOJ9hQNo08/d+1wyzk3zL0fDH9ZU2xFSEgoyIuL/qgWbT+U3rILwHnMuGdaPhu05weLnV1Yk4T/YeWHIzrLgPco5CaBu4cZU5F5PWKwMUZETEk4S2hoSlF809sx4W9IBVQ80/8mWx283pTGfMMO/tdmdXLHJljHz47Z/wzTWw/1tzeYE2r0O/NVCrk9XVuRUFGRHxLIVzz2yDRg+a23b+5/zcM5+VPvdMUpK5sEyfPjBwoHkfG2tuF3EnWdvM0UhrhsG5U1C7O9yUCq3+Cl7Vra7O7XhEkJk0aRKxsbH4+fnRtWtXfv75Z6tLEhGr+dWG6z6FhBQIudZc1XflYFgUD5mbi+6blESJS/zu22duV5ixzOUaya6kAc2jG90KVof/ti0c/slcXqDjB+YM2CEtra7Obbn9qKXPP/+cBx54gI8++oiuXbsyfvx4Zs2axbZt24iIiCjz/Rq1JFIF5OeZi1BueAXsp8FWzewM2epFsPmWvcRvdDSkpVk/3rSKSUqCESOK/qeJjr4wSVtpr5U2t8nljufW86HYc2HvPDPEnPjV3BbVD7p8BDUaWFqalSrN8OuuXbvSuXNn/vGPfwCQn59PTEwMjz/+OM8991yx/XNycsjJySl8npWVRUxMjIKMSFWQvQvWPAH7vjSf12gAvg/DTc+X+Vb7wsV4Xx/v3PqkUEEj2aXfQDZb6VcHC2abLWmitssdr7T3WO7U7+aSHL9PhrOHzG0+YdBxPMTeV+bMvJVdpRh+nZuby9q1a0lISCjc5uXlRUJCAitWrCjxPWPHjiUkJKTwFhOjuSZEqowaDaD3fHOG04AYM9gcex5GArUu/9YRd2XoCpOL2O1my0lJgeVy/7QueG3kyOKXoMo63qXvsUx+HuyeAz/eCF82hs1vmyHGP8psQbx5CzS8v8qHmIpw6yBz5MgR7HY7deoUnW65Tp06HDhwoMT3jBkzhszMzMLbnj17XFGqiLiTi+eewRs6A+8AfzSflmTTsSh1l3GRlJTSr/SVxTBgzx7zGOU9XknvcblT6fDrX2FeffhpABxYANggqi/EzYX+u6HNq+BXdpcJKaqa1QU4mq+vL76+vlaXISJWK5h7pv698FEXaJgL9wJxwGfAVnO3fGzsJZqlmEv8jhwJ/furu4wzZWQ49hjlPZ4jzlsh+edg/zew/SPI+B443zzkVwca/QWaDIXAhi4uqvJx6yBTu3ZtvL29OXjwYJHtBw8eJDIy0qKqRMSj1GoLLabBB3di3AO2+sCLwCbInw9sMhjJePLxhov+5R4fb23ZlVlUlGOPUd7jOeK85ZK9x5wSYOcncGbfhe2RCdDkYbPFUMOoHcatLy35+PjQsWNHFi1aVLgtPz+fRYsW0a1bNwsrExGPcscAeGIOp9+IgkXAOeBa8HoedrzSFK8u+Xh7nSvc3eX/cq9i4uLM0URX0g3EZoOYGPMY5T1eSe9xuHy7uZhj8i3wZSxsfNUMMb7h0PIZuGU7/GEB1B+gEONgbt0iAzBq1CgGDRpEp06d6NKlC+PHjyc7O5sHH3zQ6tJExJMkJrI6pD8vJ6Rw7byN/OHmH/ljn//RrMl2Zo+4k/TDDZj4/RN8kjyEqKgQq6ut1Ly9zSHRAwYUH6V08fOSXgMYP77opb+yjlfSexzm9D6z5WXnfy4sbArmGkhNHobo28Bb3R2cye2HXwP84x//4N133+XAgQO0a9eOiRMn0rVr13K9V/PIiEgBu92cUmbfPvPLLiL4IMNumMSjCR8SHnwEgJNng6jRZgheLZ5Q/wUnK2nel5gYM3RA6a9VZB6Zst5zRfLtcOAH2PGx2QpjnB8O5VsLGg6GJg9BcDMHnrBqqjTzyFwtBRkRuVjBfCNw4V/uftXPcF/PaYzs9z7XRp+fFdjmZf5ruvmT5kKVGg7rFHa72ScpI8PswxIXd6Hl5HKvXcnxrtqZDNj5Kez8tzm0v0BEL7P1JSYRvP0cdDJRkDlPQUZELlXqv9zfN0js9gNse//8KJPzwjqZ6zvV7ACh15pTx0vVYOTDgYVm68veL8E435fKpyY0HGS2vmj5AKdQkDlPQUZESlLmv9xPbIJt4yFtKuTnXPSCDQIbmytxh7aG0DbmfWBj8NKY7UrjzEFzxt2d/zZn4C0Q3uN868sAqOZvXX1VgILMeQoyInJVzh42p5E/tARObICzJU/Gibe/uXhlQbApCDl+4a6t10pOva7jAkY+HFx8vvVlnjkLL0D1EHO23SYPQ2grS0usShRkzlOQERGHOnvYDDQn1l+4z9wE9jMl7+9Xp3i4Cbmm8vWl8NgVG4GzRyBtCmz/GE7tuLC9VlczvDS4G6oFWFZeVaUgc56CjIg4Xb4dTu08H2wuCjmndlI4m+vFbF4Q1KzopanQNuZaUTa3nt6rZJ64YqNhwKGlZuvLnjmQn2turxYEDe8zA0zNttbWWMUpyJynICMiljmXbfa1KQg2medDTs7RkvevFgghraBmGwhpbd6HtjY7lrqrgjHtpS12ZLOZLTNpae5xmSnnGKR9Zl4uzNp6YXtYp/OtL382l7cQy5X3+9vtJ8QTEfFY1WpA7S7mrYBhmP1sjq83g03BfeZmOHcKjq40bxfzr1e05Sa0NQS3AG8f1/48JanIio1WrftgGHB4mdn6snvWhc7b1WpA7L1mgAnrYE1tctUUZEREXMlmA/8o81a374Xt+Xlwcnvx/jfZu8yp7s/sg4z/XXScamaYufTyVMAVzv1/pdx2xUYg97g56mzHx2ZQLFCzvRleYgdC9SDX1yUOpSAjIuIOvKqbnYBDrjE7lxbIzYTMjZcEnA2Qd3575kbYNePC/tVDL+pYXBByWkF1J11ad7cVG8+dgaOr4PcpsPtzsJ81t3sHmJeNmjwMtTprgsNKRH1kREQ8jWGY6/pc2nqTte3ChG2XqhFbvPUmqCl4XeW/Zy9d9+FSzuwjYxjmHC9Hzl+OO7ISjqcW/QxCW59vfbkPfLSGlidRHxkRkcrKZoMa9c1bvT9d2G7PMcPMxeHmxAbzslR2unnb99WF/b18zVlpL+1/4xdZ/hYLV67YmHcSjq42Q8vhFeZ9zpHi+/lFQt1+0PghqH2dWl8qObXIiIhUdjnHil+aytxgjqoqiW+t8/PdtL4wgqqspRkcvWJjzlFzVFHm5gvh5cRGig1n9/Ixl46ofd2FW0B9hZdKQMOvz1OQEREpgZFvttAcv3hivw1mh2Mjv4Q3XLw0w0UtOIGNLizNUJGZfe1n4cwBcyHGsxlmp+asrZC5xbzPOVzy+2o0gNrdoNb50FKzHXj7OuADEXejIHOegoyISAWcOwNZm81wUzA0/MR6OHuo5P29/c25by7uXOwfBWcPmiGlIKicueSWd6LsWgLqmyOzwtqfDy5dzWNLlaA+MiIiUnHV/CGso3m72JmD50PNRbMXFyzNcGy1easoLx8zmPhFQUA9M7QEt4SQFhDUXBPTSbkoyIiISNn865i3yIQL2/Lt5tpEl46eyjlidrgtmC/HL+rCY//IC899aqovi1w1BRkREbkyXt4Q3Ny81R9gdTVSRSnIiIiIWK0iHaWlCAUZERERK5U0dD062pyfx91WDXdDHrhevIiISCWRlGROJnjpwpv79pnbk5KsqcuDKMiIiIhYwW43W2JKmgWlYNvIkeZ+UioFGRERESukpBRvibmYYcCePeZ+UioFGREREStkZDh2vypKQUZERMQKUeWcpbi8+1VRCjIiIiJWiIszRyeVNimgzWYuvBkX59q6PIyCjIiIiBW8vc0h1lA8zBQ8Hz9e88mUQUFGRETEKomJMHs21KtXdHt0tLld88iUSRPiiYiIWCkxEfr318y+V0hBRkRExGre3hAfb3UVHkmXlkRERMRjKciIiIiIx1KQEREREY+lPjIiIpdht6sPpog7U5ARESlFUpK5pt/Fy+FER5tTf2hUrIh70KUlEZESJCXBgAHF1/Tbt8/cnpRkTV0iUpSCjIjIJex2syXGMIq/VrBt5EhzPxGxloKMiMglUlKKt8RczDBgzx5zPxGxloKMiMglMjIcu5+IOI+CjIjIJaKiHLufiDiPgoyIyCXi4szRSZcuSFzAZoOYGHM/EbGWgoyIyCW8vc0h1lA8zBQ8Hz9e88mIuAMFGRGREiQmwuzZUK9e0e3R0eZ2zSMj4h40IZ6ISCkSE6F/f83sK+LOFGRERC7D2xvi462uQkRKo0tLIiIi4rEUZERERMRjKciIiIiIx1KQEREREY+lICMiIiIeS0FGREREPJaCjIiIiHgsBRkRERHxWAoyIiIi4rEq/cy+hmEAkJWVZXElIiIiUl4F39sF3+OlqfRB5uTJkwDExMRYXImIiIhU1MmTJwkJCSn1dZtRVtTxcPn5+ezfv5+goCBsNpvDjpuVlUVMTAx79uwhODjYYcf1NPoc9BmAPoMC+hz0GRTQ53D1n4FhGJw8eZK6devi5VV6T5hK3yLj5eVFdHS0044fHBxcZX9JL6bPQZ8B6DMooM9Bn0EBfQ5X9xlcriWmgDr7ioiIiMdSkBERERGPpSBzhXx9fXnppZfw9fW1uhRL6XPQZwD6DAroc9BnUECfg+s+g0rf2VdEREQqL7XIiIiIiMdSkBERERGPpSAjIiIiHktBRkRERDyWgoyDxMbGYrPZitzeeustq8uyRE5ODu3atcNms5Gammp1OS516623Ur9+ffz8/IiKiuL+++9n//79VpflUunp6QwZMoSGDRvi7+9P48aNeemll8jNzbW6NJd644036N69OwEBAYSGhlpdjstMmjSJ2NhY/Pz86Nq1Kz///LPVJbnU0qVLueWWW6hbty42m4158+ZZXZLLjR07ls6dOxMUFERERAS33XYb27Ztc9r5FGQc6NVXXyUjI6Pw9vjjj1tdkiWeeeYZ6tata3UZlujTpw9ffPEF27ZtY86cOezcuZMBAwZYXZZLbd26lfz8fD7++GM2bdrE+++/z0cffcTzzz9vdWkulZuby5133smjjz5qdSku8/nnnzNq1CheeuklfvnlF9q2bUvfvn05dOiQ1aW5THZ2Nm3btmXSpElWl2KZJUuWMGzYMFauXMmCBQvIy8vjxhtvJDs72zknNMQhGjRoYLz//vtWl2G5b7/91mjRooWxadMmAzDWrVtndUmWmj9/vmGz2Yzc3FyrS7HUO++8YzRs2NDqMiwxefJkIyQkxOoyXKJLly7GsGHDCp/b7Xajbt26xtixYy2syjqAMXfuXKvLsNyhQ4cMwFiyZIlTjq8WGQd66623qFWrFu3bt+fdd9/l3LlzVpfkUgcPHmTo0KFMnTqVgIAAq8ux3LFjx5g2bRrdu3enevXqVpdjqczMTMLCwqwuQ5woNzeXtWvXkpCQULjNy8uLhIQEVqxYYWFlYrXMzEwAp/0NUJBxkCeeeIKZM2eyePFiHn74Yd58802eeeYZq8tyGcMwGDx4MI888gidOnWyuhxLPfvss9SoUYNatWqxe/du5s+fb3VJltqxYwcffPABDz/8sNWliBMdOXIEu91OnTp1imyvU6cOBw4csKgqsVp+fj4jR46kR48etGrVyinnUJC5jOeee65YB95Lb1u3bgVg1KhRxMfH06ZNGx555BHGjRvHBx98QE5OjsU/xdUp72fwwQcfcPLkScaMGWN1yQ5Xkd8DgKeffpp169bxww8/4O3tzQMPPIBRCSbQrujnALBv3z769evHnXfeydChQy2q3HGu5DMQqcqGDRvGxo0bmTlzptPOoSUKLuPw4cMcPXr0svs0atQIHx+fYts3bdpEq1at2Lp1K82bN3dWiU5X3s/grrvu4quvvsJmsxVut9vteHt7c++99/LZZ585u1SnuZrfg7179xITE8Py5cvp1q2bs0p0iYp+Dvv37yc+Pp7rrruOKVOm4OXl+f9uupLfhSlTpjBy5EhOnDjh5OqslZubS0BAALNnz+a2224r3D5o0CBOnDhRJVsmbTYbc+fOLfJ5VCXDhw9n/vz5LF26lIYNGzrtPNWcduRKIDw8nPDw8Ct6b2pqKl5eXkRERDi4Ktcq72cwceJEXn/99cLn+/fvp2/fvnz++ed07drVmSU63dX8HuTn5wN4fMscVOxz2LdvH3369KFjx45Mnjy5UoQYuLrfhcrOx8eHjh07smjRosIv7vz8fBYtWsTw4cOtLU5cyjAMHn/8cebOnUtycrJTQwwoyDjEihUrWLVqFX369CEoKIgVK1bw5JNPct9991GzZk2ry3OJ+vXrF3keGBgIQOPGjYmOjraiJJdbtWoVq1evpmfPntSsWZOdO3fy4osv0rhxY49vjamIffv2ER8fT4MGDfj73//O4cOHC1+LjIy0sDLX2r17N8eOHWP37t3Y7fbCOZWaNGlS+P9HZTNq1CgGDRpEp06d6NKlC+PHjyc7O5sHH3zQ6tJc5tSpU+zYsaPweVpaGqmpqYSFhRX7O1lZDRs2jOnTpzN//nyCgoIK+0iFhITg7+/v+BM6ZSxUFbN27Vqja9euRkhIiOHn52e0bNnSePPNN42zZ89aXZpl0tLSqtzw6/Xr1xt9+vQxwsLCDF9fXyM2NtZ45JFHjL1791pdmktNnjzZAEq8VSWDBg0q8TNYvHix1aU51QcffGDUr1/f8PHxMbp06WKsXLnS6pJcavHixSX+dx80aJDVpblMaf//T5482SnnUx8ZERER8ViV48K1iIiIVEkKMiIiIuKxFGRERETEYynIiIiIiMdSkBERERGPpSAjIiIiHktBRkRERDyWgoyIiIh4LAUZERER8VgKMiIiIuKxFGRERETEYynIiIhHOXz4MJGRkbz55puF25YvX46Pjw+LFi2ysDIRsYIWjRQRj/Ptt99y2223sXz5cpo3b067du3o378/7733ntWliYiLKciIiEcaNmwYCxcupFOnTmzYsIHVq1fj6+trdVki4mIKMiLikc6cOUOrVq3Ys2cPa9eupXXr1laXJCIWUB8ZEfFIO3fuZP/+/eTn55Oenm51OSJiEbXIiIjHyc3NpUuXLrRr147mzZszfvx4NmzYQEREhNWliYiLKciIiMd5+umnmT17Nr/++iuBgYH07t2bkJAQvv76a6tLExEX06UlEfEoycnJjB8/nqlTpxIcHIyXlxdTp04lJSWFDz/80OryRMTF1CIjIiIiHkstMiIiIuKxFGRERETEYynIiIiIiMdSkBERERGPpSAjIiIiHktBRkRERDyWgoyIiIh4LAUZERER8VgKMiIiIuKxFGRERETEYynIiIiIiMf6/2XkdS8x63SxAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Overfitting & underfitting"
      ],
      "metadata": {
        "id": "Qb6d4rl3jPAX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# separate fitting and validation data\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_fit, x_val, y_fit, y_val = train_test_split(x, y, test_size=0.2, random_state=1)"
      ],
      "metadata": {
        "id": "a0C3rokhjP-L"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# assess performance on validation sets for different hyperparameter values\n",
        "from sklearn.metrics import mean_squared_error as mse\n",
        "fit_MSEs = []\n",
        "validation_MSEs = []"
      ],
      "metadata": {
        "id": "V00EUbjMkdNe"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for poly_degree in range(1,6):\n",
        "  # set hyper-parameter value\n",
        "  pipe['poly'].degree = poly_degree\n",
        "  # fit & predict\n",
        "  pipe.fit(x_fit, y_fit)\n",
        "  y_pred_fit = pipe.predict(x_fit)\n",
        "  y_pred_val = pipe.predict(x_val)\n",
        "  # compute scores and append\n",
        "  fit_MSE = mse(y_fit, y_pred_fit)\n",
        "  validation_MSE = mse(y_val, y_pred_val)\n",
        "  fit_MSEs.append(fit_MSE), validation_MSEs.append(validation_MSE)"
      ],
      "metadata": {
        "id": "8T-mgWNqkfPZ"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# plot validation curve\n",
        "from matplotlib import pyplot as plt\n",
        "plt.figure()\n",
        "plt.plot(np.arange(1,6), fit_MSEs, 'b--', label='fitting MSEs')\n",
        "plt.plot(np.arange(1,6), validation_MSEs, 'g--', label='validation MSEs')\n",
        "plt.legend(), plt.xlabel('Polynomial degree'), plt.ylabel('MSE')"
      ],
      "metadata": {
        "id": "LhVKxSQPkiu9",
        "outputId": "c9f594d5-6057-44e6-c932-d42c0fabd6f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        }
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<matplotlib.legend.Legend at 0x7e7ee17bf550>,\n",
              " Text(0.5, 0, 'Polynomial degree'),\n",
              " Text(0, 0.5, 'MSE'))"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train/validation/test split"
      ],
      "metadata": {
        "id": "WzHBGppgk-eK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# train-validate-test split\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=1)\n",
        "x_fit, x_val, y_fit, y_val = train_test_split(x_train, y_train, test_size=0.3, random_state=1)"
      ],
      "metadata": {
        "id": "el7TSgsVk_fb"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Number of samples in fitting set: ', x_fit.shape[0])\n",
        "print('Number of samples in validation set: ', x_val.shape[0])\n",
        "print('Number of samples in test set: ', x_test.shape[0])"
      ],
      "metadata": {
        "id": "_PlS_I6mlD5U",
        "outputId": "8999ea69-064d-46a3-9337-5b9c63f85587",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of samples in fitting set:  14\n",
            "Number of samples in validation set:  6\n",
            "Number of samples in test set:  5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## K-fold cross-validation"
      ],
      "metadata": {
        "id": "4Es6IP34lGtg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# separate training and test data\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=1)"
      ],
      "metadata": {
        "id": "Wlskg7ESlHYF"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# generate k=3 folds on training data\n",
        "from sklearn.model_selection import KFold\n",
        "kfold = KFold(n_splits = 3, shuffle = True, random_state = 1)"
      ],
      "metadata": {
        "id": "jwYjOeNjlJdU"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# compute validation performances using the K folds\n",
        "from sklearn.metrics import mean_squared_error as mse\n",
        "overall_fit_MSEs = [] # overall errors for different polynomial degrees\n",
        "overall_val_MSEs = []\n",
        "\n",
        "max_polyDegree = 5\n",
        "for poly_degree in range(1,max_polyDegree+1):# loop over hyperparameters\n",
        "    pipe['poly'].degree = poly_degree\n",
        "\n",
        "    split_fit_MSEs = [] # errors for different splits\n",
        "    split_val_MSEs = []\n",
        "\n",
        "    for fit_indices, val_indices in kfold.split(x_train): # loop over splits\n",
        "        x_fit = x_train[fit_indices]\n",
        "        y_fit = y_train[fit_indices]\n",
        "        x_val = x_train[val_indices]\n",
        "        y_val = y_train[val_indices]\n",
        "\n",
        "        # fit & predict\n",
        "        pipe.fit(x_fit, y_fit)\n",
        "        y_pred_fit = pipe.predict(x_fit)\n",
        "        y_pred_val = pipe.predict(x_val)\n",
        "\n",
        "        # compute average error and append\n",
        "        split_fit_MSEs.append(mse(y_fit, y_pred_fit))\n",
        "        split_val_MSEs.append(mse(y_val, y_pred_val))\n",
        "\n",
        "    overall_fit_MSEs.append(np.mean(split_fit_MSEs))\n",
        "    overall_val_MSEs.append(np.mean(split_val_MSEs))"
      ],
      "metadata": {
        "id": "GCZe1XeClOuP"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# plot validation curve\n",
        "from matplotlib import pyplot as plt\n",
        "plt.figure()\n",
        "plt.plot(np.arange(1,max_polyDegree+1), overall_fit_MSEs, 'b--', label='fitting MSEs')\n",
        "plt.plot(np.arange(1,max_polyDegree+1), overall_val_MSEs, 'g--', label='validation MSEs')\n",
        "plt.legend(), plt.xlabel('Polynomial degree'), plt.ylabel('MSE')\n",
        "plt.xticks(np.arange(1,max_polyDegree+1))\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "dwDFaGxTs3ff",
        "outputId": "355377aa-47f0-4a26-e0ea-b5f95668a814",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        }
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Regularization"
      ],
      "metadata": {
        "id": "XUuLreOB9h_l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# read data\n",
        "import numpy as np\n",
        "data = np.loadtxt('quadratic_raw_data.csv', delimiter=',')\n",
        "x = data[:,0,None]; y = data[:,1,None]\n",
        "\n",
        "# separate training data\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=1)"
      ],
      "metadata": {
        "id": "xZEJcVeo9kCo"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Ordinary Least Squares Regression"
      ],
      "metadata": {
        "id": "QPFM7CXqAHNr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# create pipeline for quadratic fit via OLS\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "pipe_OLS = Pipeline([('poly', PolynomialFeatures(degree=10, include_bias=False)),\n",
        "                 ('scaler', StandardScaler()),\n",
        "                 ('model', LinearRegression())])"
      ],
      "metadata": {
        "id": "dCMrQrik_OL6"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# fit pipeline and predict\n",
        "pipe_OLS.fit(x_train, y_train)\n",
        "y_predicted_train_OLS = pipe_OLS.predict(x_train)\n",
        "y_predicted_test_OLS = pipe_OLS.predict(x_test)"
      ],
      "metadata": {
        "id": "IFW2n8-T_Qv7"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# performance metrics\n",
        "from sklearn.metrics import mean_squared_error as mse\n",
        "\n",
        "print('OLS Training metric (mse) = ', mse(y_train, y_predicted_train_OLS))\n",
        "print('OLS Test metric (mse) = ', mse(y_test, y_predicted_test_OLS))"
      ],
      "metadata": {
        "id": "lXtJfZPm_R1E",
        "outputId": "1204f7ab-dbac-406b-8bc3-05565c235223",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "OLS Training metric (mse) =  0.3130714280718393\n",
            "OLS Test metric (mse) =  6.950826348841896\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot predictions\n",
        "y_predicted_OLS = pipe_OLS.predict(x)\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "plt.figure()\n",
        "plt.plot(x_train,y_train, 'bo', label='raw training data')\n",
        "plt.plot(x_test,y_test, 'ro', label='raw test data')\n",
        "plt.plot(x,y_predicted_OLS, color='orange', label='OLS fit')\n",
        "plt.legend()\n",
        "plt.xlabel('x'), plt.ylabel('y')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "DYzaMTiU_TFv",
        "outputId": "d498e35c-82c6-4e7a-987c-b1e1a568a071",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        }
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# print coefficients\n",
        "print(pipe_OLS['model'].coef_)"
      ],
      "metadata": {
        "id": "c2RcNaip_W_7",
        "outputId": "3a60e6ed-8a25-4147-a220-1097de730c65",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-1.96123403e+00 -1.70142862e+01  1.61112987e+02  5.53503743e+02\n",
            "  -1.47185815e+03 -5.24500174e+03  9.88824037e+02  1.58830783e+04\n",
            "   1.72273909e+04  5.73349950e+03]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Ridge Regression"
      ],
      "metadata": {
        "id": "VZhIZOo0BJC4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# create pipeline for quadratic fit via ridge model\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.linear_model import Ridge\n",
        "\n",
        "pipe_L2 = Pipeline([('poly', PolynomialFeatures(degree=10,include_bias=False)),\n",
        "                    ('scaler', StandardScaler()),\n",
        "                    ('model', Ridge(alpha=0.1))])"
      ],
      "metadata": {
        "id": "UwlkE13H_YyZ"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# fit pipeline and predict\n",
        "pipe_L2.fit(x_train, y_train)\n",
        "y_predicted_train_L2 = pipe_L2.predict(x_train)\n",
        "y_predicted_test_L2 = pipe_L2.predict(x_test)"
      ],
      "metadata": {
        "id": "xugxgoPd_Zw1"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# performance metrics\n",
        "from sklearn.metrics import mean_squared_error as mse\n",
        "\n",
        "print('L2 Training metric (mse) = ', mse(y_train, y_predicted_train_L2))\n",
        "print('L2 Test metric (mse) = ', mse(y_test, y_predicted_test_L2))"
      ],
      "metadata": {
        "id": "Z9i4LL_w_a_V",
        "outputId": "e37d0659-645d-43f8-cb38-bc2b6fe98bb9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "L2 Training metric (mse) =  3.197250376366914\n",
            "L2 Test metric (mse) =  3.44747159916285\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot predictions\n",
        "y_predicted_L2 = pipe_L2.predict(x)\n",
        "\n",
        "plt.figure()\n",
        "plt.plot(x_train,y_train, 'bo', label='raw training data')\n",
        "plt.plot(x_test,y_test, 'ro', label='raw test data')\n",
        "plt.plot(x,y_predicted_L2, color='orange', label='Ridge fit')\n",
        "plt.legend()\n",
        "plt.xlabel('x'), plt.ylabel('y')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Msuvs_40_1Ib",
        "outputId": "af6ac218-adf4-40aa-aefb-93f50b880a7a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        }
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# print coefficients\n",
        "print(pipe_L2['model'].coef_)"
      ],
      "metadata": {
        "id": "S8tPaZpu_3Fu",
        "outputId": "e7443de0-8fc1-425e-e724-d845eaa2b097",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 0.27326159  3.49270237 -0.35861471  0.59074719 -1.20522322  1.9065296\n",
            " -1.80640701  0.86690221  0.87244476 -3.20685088]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Lasso Regression"
      ],
      "metadata": {
        "id": "4QIEpSNKBO00"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# create pipeline for quadratic fit via ridge model\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.linear_model import Lasso\n",
        "\n",
        "pipe_L1 = Pipeline([('poly', PolynomialFeatures(degree=10,include_bias=False)),\n",
        "                    ('scaler', StandardScaler()),\n",
        "                    ('model', Lasso(alpha=0.1))])"
      ],
      "metadata": {
        "id": "UKz4tkJm_5s9"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# fit pipeline and predict\n",
        "pipe_L1.fit(x_train, y_train)\n",
        "y_predicted_train_L1 = pipe_L1.predict(x_train)\n",
        "y_predicted_test_L1 = pipe_L1.predict(x_test)"
      ],
      "metadata": {
        "id": "jkufDryh_6qW"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# performance metrics\n",
        "from sklearn.metrics import mean_squared_error as mse\n",
        "\n",
        "print('L1 Training metric (mse) = ', mse(y_train, y_predicted_train_L1))\n",
        "print('L1 Test metric (mse) = ', mse(y_test, y_predicted_test_L1))"
      ],
      "metadata": {
        "id": "an3lBBUe_7py",
        "outputId": "d1be2cfc-b7a9-4a43-b879-b38a0507c6fc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "L1 Training metric (mse) =  3.5966292903741506\n",
            "L1 Test metric (mse) =  3.9280112362976114\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot predictions\n",
        "y_predicted_L1 = pipe_L1.predict(x)\n",
        "\n",
        "plt.figure()\n",
        "plt.plot(x_train,y_train, 'bo', label='raw training data')\n",
        "plt.plot(x_test,y_test, 'ro', label='raw test data')\n",
        "plt.plot(x,y_predicted_L1, color='orange', label='Lasso fit')\n",
        "plt.legend()\n",
        "plt.xlabel('x'), plt.ylabel('y')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "K9l6IUCv_8nF",
        "outputId": "a6451a6b-0ac2-47b7-b5a6-1e1d9522c09c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        }
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# print coefficients\n",
        "print(pipe_L1['model'].coef_)"
      ],
      "metadata": {
        "id": "5xPr8STt__v0",
        "outputId": "f256b7d4-bde9-460e-db28-c7cc64e21b23",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-0.          4.38109181 -0.09273215  1.679048   -0.          0.\n",
            " -0.          0.          0.         -0.        ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Combined plot"
      ],
      "metadata": {
        "id": "PTVnNsHKDdEr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure()\n",
        "plt.plot(x_train,y_train, 'bo', label='raw training data')\n",
        "plt.plot(x_test,y_test, 'ro', label='raw test data')\n",
        "plt.plot(x,y_predicted_OLS, color='orange', label='OLS fit')\n",
        "plt.plot(x,y_predicted_L2, label='Ridge fit')\n",
        "plt.plot(x,y_predicted_L1, label='Lasso fit')\n",
        "plt.legend()\n",
        "plt.xlabel('x'), plt.ylabel('y')"
      ],
      "metadata": {
        "id": "8-RmZDHMDQoT",
        "outputId": "253f5e47-c972-434f-c817-c3e7172fce0f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        }
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Text(0.5, 0, 'x'), Text(0, 0.5, 'y'))"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Hyperparameter optimization via GridSearchCV"
      ],
      "metadata": {
        "id": "71OVljZJEIOI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# read data\n",
        "import numpy as np\n",
        "data = np.loadtxt('quadratic_raw_data.csv', delimiter=',')\n",
        "x = data[:,0,None]; y = data[:,1,None]"
      ],
      "metadata": {
        "id": "zOqqGu2xEKMP"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# create pipeline\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "pipe = Pipeline([('poly', PolynomialFeatures(include_bias=False)),\n",
        "                 ('scaler', StandardScaler()),\n",
        "                 ('model', LinearRegression())])"
      ],
      "metadata": {
        "id": "SqfrGngDEN4Y"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# separate fitting and validation data\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=1)"
      ],
      "metadata": {
        "id": "i1UIGKNDEPlD"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# find optimal hyperparameter via GridSearchCV\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "param_grid = {'poly__degree': np.arange(1,6)}\n",
        "gs = GridSearchCV(pipe, param_grid, scoring='neg_mean_squared_error', cv=3)\n",
        "gs.fit(x_train, y_train)\n",
        "\n",
        "print('Optimal hyperparameter:', gs.best_params_)"
      ],
      "metadata": {
        "id": "kxN81ZLlES6r",
        "outputId": "856e32e3-61b0-4551-b1b1-c2e0b9e1115d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Optimal hyperparameter: {'poly__degree': np.int64(2)}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# get best model and predict\n",
        "pipe_best = gs.best_estimator_\n",
        "y_predicted_train = pipe_best.predict(x_train) # can also use gs.predict(x_train)\n",
        "y_predicted_test = pipe_best.predict(x_test) # can also use gs.predict(x_test)"
      ],
      "metadata": {
        "id": "uY1q4N6HERoa"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# performance metrics\n",
        "from sklearn.metrics import mean_squared_error as mse\n",
        "\n",
        "print('Training metric (mse) = ', mse(y_train, y_predicted_train))\n",
        "print('Test metric (mse) = ', mse(y_test, y_predicted_test))"
      ],
      "metadata": {
        "id": "_eZrrbP7EWRz",
        "outputId": "a575ac9c-05f6-4fa2-b2a9-d7a3ef3f03a1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training metric (mse) =  3.789080699843305\n",
            "Test metric (mse) =  3.5558636543779985\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot predictions\n",
        "y_predicted = pipe_best.predict(x)\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "plt.figure()\n",
        "plt.plot(x_train,y_train, 'bo', label='raw training data')\n",
        "plt.plot(x_test,y_test, 'ro', label='raw test data')\n",
        "plt.plot(x,y_predicted, color='orange', label='quadratic fit')\n",
        "plt.legend()\n",
        "plt.xlabel('x'), plt.ylabel('y')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "amVMrSomEYBq",
        "outputId": "79509d04-2b27-42a8-84a5-08afcb89354a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        }
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}